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一个自我监督的对抗性模糊面部识别网络,用于边缘设备

Hanwen Zhang¹, Myun Kim¹, Baitong Li²

¹Department of Industrial Design, Pukyong National University, 45, Yongso-ro, Nam-Gu, Busan 48513, Republic of Korea.

Journal of imaging

|July 25, 2025

概括

这项研究引入了一种新的面部识别模型,它在模糊和动态条件下表现出色,改善了人类活动识别. 先进的生成对抗网络 (GAN) 和消除模糊的技术在现实应用中提高了准确性和回忆率.

科学领域:

计算机视觉计算机视觉
人工智能的人工智能
机器学习机器学习

背景情况:

人类活动识别 (HAR) 对于智能监控和健康监测至关重要.
面部识别对于基于视觉的HAR至关重要,但与模糊和动态图像作斗争.
由于图像质量限制,现有的模型在现实世界HAR场景中缺乏稳定性.

研究的目的:

开发一个快速而准确的面部识别模型,用于增强HAR.
在具有挑战性,动态和模糊条件下提高面部识别性能.
解决现有模型在现实世界HAR应用中的局限性.

主要方法:

使用生成对抗网络 (GAN) 作为核心算法.
通过分解全局损失函数并整合特征金字塔来优化GAN模块.
集成的消除模糊技术,以提高模糊和动态图像场景的识别.

主要成果:

在多个面部识别数据集中实现了高准确度和回忆率.
报告了平均召回率为87.40%和准确率为81.06% (YTF) 和79.77% (WiderFace).
在HAR中有效处理动态和模糊的面部图像.

结论:

拟议的模型在具有挑战性的HAR环境中显著提高了面部识别.
这种新的方法显示出在智能监控和人机交互方面的现实应用的巨大潜力.
该研究成功地解决了HAR目前面部识别技术的关键局限性.

关键词:

功能金字塔是一个特征金字塔.消除加工过程中的模糊.面部识别功能面部识别功能生成式对抗网络 (GAN) 是一种产生式对抗网络.全球损失函数的全球损失函数人类活动的认可人类活动的认可

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